Use cases

A centralised runtime and control plane for designing, deploying, orchestrating, observing and governing AI agents and multi-agent workflows on Azure. Eight decisions carry the design: the control plane and the execution plane are separated absolutely, and the interface between them is a signed immutable bundle rather than a shared database; durable execution runs on Azure Durable Functions behind a workflow-engine port, so the engine can be replaced without touching agent code; every model call goes through API Management as an AI gateway, so metering, token limits, semantic caching, circuit breaking and provider fallback are policy rather than application code; tools are out-of-process and least-privileged, with credentials brokered per call and code execution confined to Hyper-V isolated session sandboxes; policy is authored centrally as signed Rego bundles and evaluated locally in every worker, so a control-plane outage cannot make policies fail open; delivery is at-least-once with idempotency pushed to the handler; memory is four stores rather than one, each permission-filtered and audited; and budget is reserved at admission, so a run cannot start that it cannot afford to finish.

A multi-tenant AI Executive Office on Azure, architected as a decision intelligence platform rather than a chatbot with company data: a governed conversational layer over enterprise systems, specialist agents, decision intelligence and controlled execution, built for a sovereign public-sector scenario and for the commercial tenants that come after it.

A multi-tenant AI Executive Office that runs entirely inside the customer's own data centre — no cloud plane, no vendor API call, no weight or prompt that leaves the building — architected as a decision intelligence platform rather than a chatbot with company data: a governed conversational layer over enterprise systems, specialist agents, decision intelligence and controlled execution, built for a sovereign public-sector scenario and for the commercial tenants that come after it.

The north–south front door for a multi-tenant SaaS that other people build on. Every SaaS product with a public API has one of these and almost nobody outside the company that runs it ever sees it: when a shop's checkout calls a payments API, when a bot posts into a team chat workspace, when a logistics dashboard pulls shipment status, the call does not land on the service that answers it — it lands here. This one carries 50,000 developer organisations, 500,000 live credentials, 40 mTLS partners and roughly 10 billion requests a day across 800 published routes over 120 upstream services, on a global anycast load balancer and Cloud Armor at the edge, an Envoy fleet on GKE as the request plane, Spanner for versioned configuration, Memorystore for counters, and Pub/Sub into BigQuery for evidence.

The credential control plane and verification data plane for a developer platform: the service that issues the string a customer pastes into their CI config, decides on every one of 250,000 requests per second whether that string is still valid and what it carries, and takes it away faster than whoever found it in a public commit can use it. It holds 2.5 million live credentials across 400,000 tenants in three regions, and it is built from Cloud Spanner for credential and revocation state, Pub/Sub for propagation, Cloud KMS/HSM for the digest pepper, Cloud Storage for projection snapshots, Memorystore and Bigtable for the derived tiers, BigQuery for audit, and an Envoy extauthz sidecar plus in-process libraries for verification itself.

A shared audit log for the whole estate: it accepts security- and compliance-relevant events from every application, platform service and administrative surface, holds them so that nobody — including its own operators and the subscription's administrators — can alter or remove one inside its retention period, and answers investigative and regulatory questions against thirteen months of that history. The requirement named Amazon Web Services; this set answers it on Azure and open source, substituting Blob immutable containers with a locked time-based retention policy for S3 Object Lock, Apache Kafka on Strimzi for Kinesis, Key Vault Managed HSM for KMS and CloudHSM, Azure Confidential Ledger for the separately administered checkpoint store, ClickHouse for the online index, Trino for the archive path, and Presidio with a compiled rule set for pre-commit secret detection.

A platform that copies 120 datastores into storage it cannot delete from, and then proves every day that the copies turn back into working systems within a stated time. It runs on hardware the organisation owns: Ceph object storage with Object Lock in two data centres, WORM tape at a third site, each engine's own backup tool behind one adapter contract, and an isolated cluster where restores are rehearsed through the same path an emergency uses.

The system that keeps every X.509 certificate a mid-size B2B SaaS depends on from ever expiring in front of a customer. Two populations live under it and behave nothing alike: 24,000 customer-owned domains pointed at the platform by tenants who expect a padlock in browsers the platform has no relationship with, and 3,800 internal workloads authenticating each other over mTLS under a certificate authority the platform runs itself. It issues, renews, installs, distributes trust, revokes, and keeps the evidence — on AWS Private CA under CloudHSM custody for the private hierarchy, an ACME client against two pre-validated public CA accounts for customer domains, EKS and Lambda for the control and verification planes, DynamoDB as the registry, S3 with Object Lock as the ledger, and EventBridge as the lifecycle spine.

A customer cancels a subscription. That edit lands in exactly one place — an operational PostgreSQL database — and the product shows it in five: the analytics dashboard the board reads, the search box, the "your plan changed" email, fourteen partner webhooks, and the support console an agent is looking at while the customer is still on the phone. Something has to carry the change from the one place it was written to the many places it is read. When that something is a nightly batch job, users call the product broken; when it is a replication job written per consumer, the fifth consumer costs what the first did all over again and the first bad transformation is repaired with hand-written SQL against production. This package is that carrier, built for an assumed mid-market SaaS: 4,000 tenants, 12 operational PostgreSQL databases, 180 replicated tables, 9,000 row changes a second with a 4× burst for 30 minutes, 780 million change events a day, and a p95 commit-to-sink lag of five seconds — on Datastream for log-based capture, Pub/Sub as the change log, Dataflow for transform and apply, BigQuery as the mirror and changelog sink, Cloud Storage for a thirteen-month archive, and Spanner for control state.

The internal platform that goes looking for outages on purpose. Everyone has been on the receiving end of the failure it exists to find: the app that streams perfectly for six weeks and then, on a Friday night with a record audience, shows a spinner — because of a retry storm behind a slow recommendation call, a cache tier that was supposed to be optional, or a failover that worked in the runbook and not in the region. This platform lets a service owner state a falsifiable hypothesis about how their service behaves when a dependency degrades, inject exactly that degradation into a bounded slice of live traffic, watch the service's own steady-state signals, and have the fault removed automatically the moment real customers start to hurt. The operating context is a consumer subscription streaming service — an assumed 140 million subscribers, 900 microservices, 40,000 pods across six regional GKE clusters in three regions — built from Cloud Run and GKE for the control plane, Spanner for guardrail and lease state, Cloud Monitoring for the high-resolution steady-state path, BigQuery for evidence and coverage, Workflows for approvals and game-day sequencing, and open-source fault injectors in the data plane.

An engineer pushes a commit, switches to Slack, and ninety seconds later a green check appears next to their pull request. That check is the most-used piece of software in their working day and the one they think about least. Behind it, a stranger's code — because a pull request from a fork is a stranger's code — has just been compiled, tested and thrown away on the company's own machines, and a signed statement now exists saying which commit produced which container image. This package is the platform behind that check — for an assumed product company of 4,200 engineers in 610 teams across 38,000 repositories, running 210,000 pipeline runs and 1.9M jobs a day, peaking at 3,200 jobs a minute with 40,000 concurrent slots, 480 TB of retained artefacts and 6 TB a day of raw logs — built on nested-virtualisation-capable EC2 instances running a microVM hypervisor, SQS and Kinesis for run state, Aurora PostgreSQL for run metadata, S3 for content-addressed artefacts and an Object-Lock transparency log, a hardware-backed KMS key for the attestor, and OIDC workload identity federation for job-scoped credentials.

Everyone has used this system without being told its name. The cookie banner. The "manage your ad preferences" screen three taps into an app. The email footer that says unsubscribe and sometimes means it. The "download your data" button that mails a zip the next day. The "delete my account" flow that warns you it is permanent, and then — somewhere behind the screen — has to make it permanent across forty internal systems and sixty vendors that were each sent a copy of you years ago. The screens are easy. What is hard is that each of them is a claim about the behaviour of an entire estate, made to a person who has no way to check it, enforced by a company whose incentive is to use the data, and audited by a regulator who will ask what was permitted on a specific Tuesday eighteen months ago. This package is the internal consent and privacy plane for an assumed consumer SaaS: 180 million registered subjects, 40 internal processing systems, 60 external recipients, 9 regional deployments across 3 jurisdictional boundaries, 120,000 permission decisions a second at steady state and 25,000 rights requests a day — on DynamoDB for the append-only consent ledger and its projection, Aurora Global for the no-personal-data purpose registry, EventBridge for withdrawal fan-out, Step Functions for rights-case orchestration, KMS per-subject keys for crypto-shredding, and S3 Object Lock for tamper-evident evidence.

A global consumer messaging platform architected around one commitment: the device, not the server, is the system of record. Clients hold a persistent authenticated socket, the platform accepts a message and assigns its authoritative order, fanout drives delivery per recipient device, and the ciphertext is deleted the moment delivery is acknowledged. Message content is encrypted end to end with the Signal protocol, so the platform routes what it cannot read.

One trusted path from a source commit to a running workload, and machine-checkable evidence that the path was followed. The requirement named AWS as its target; this set answers it with an open-source stack the team runs on its own hardware — Harbor and MinIO for artifacts, Sigstore (Fulcio, Rekor, Cosign) for signing and transparency, Tekton Chains for provenance, Syft for SBOMs, Trivy and Grype for scanning, OSV and distro feeds reconciled into one advisory record, OPA for policy, Kafka for evidence distribution, ClickHouse for the component index, PostgreSQL for registry metadata, Vault with a PKCS#11 HSM for key custody, SPIRE and Keycloak for identity.

The platform that answers the question every cloud invoice refuses to: not what did we spend, but whose was it. The provider bills an account, a subscription or a project; the organisation wants to know what a team, a product and a customer cost. Everyone who has sat in a quarterly review has seen the failure it exists to prevent — a number nobody can reproduce, a spike that lands in a bucket called "untagged", a shared Kubernetes cluster whose bill arrives as one line for forty teams, and a finance figure that disagrees with the engineering figure in the same meeting. This platform ingests billing data from three clouds plus licence and SaaS spend, attributes every cost record to an accountable owner, apportions what nobody incurred alone, and publishes a frozen monthly statement that engineering and finance both believe. It starts as showback, where the number is informational, with a deliberate later path to chargeback, where it moves real budget. The operating context is an assumed estate of ~1,200 billing scopes, ~40,000 active resources, 450 engineering teams across 28 product lines, and roughly $180M a year of cloud spend plus $40M of licence and SaaS spend, built on ADLS Gen2 for the immutable raw zone, Azure Data Explorer for the cost fact store, Databricks for conformance and allocation, Azure SQL for effective-dated ownership and frozen statements, Event Hubs for container usage telemetry, and Entra ID for accountability-scoped access.

One governed, semantic, secured view of a customer, assembled at query time from Salesforce, SAP S/4HANA, Oracle Billing, ServiceNow, Marketing Cloud, an Azure lakehouse and a digital event stream. Denodo is the enterprise logical data layer; the requirement's illustrative stack named Snowflake, AWS S3, Apigee, Okta, Reltio and Informatica, and this set answers it on Azure and open source instead — Databricks with Delta Lake on ADLS Gen2 for the lakehouse, Azure Event Hubs on the Kafka protocol for streaming, Azure API Management for the gateway, Microsoft Entra ID for identity, Zingg on Spark plus a PostgreSQL crosswalk in place of a commercial MDM, Soda Core for quality rules, Denodo's Presto-based Embedded MPP for lake acceleration, Microsoft Purview federating the Denodo catalogue, and Azure Monitor with Prometheus, Grafana and Sentinel for observability.

The outage that never gets a status page: a table that is present, fresh-looking and wrong. The job succeeded, the dashboard rendered, the pricing model scored, and nobody was paged — because nothing failed. Four weeks later a merchant disputes an invoice and someone discovers a currency column silently changed units. This is the architecture of the service that judges whether data in a consumer super-app's lakehouse is fit to use, attaches that judgement to the data itself, and decides whether bad data is allowed to circulate: 12,000 datasets, 2.4 PB, 180,000 assertion evaluations a day, and 12,000 state reads a second from the orchestrator tasks that ask, before every run, whether their input is safe. Built from Delta Lake table versions as the subject of every verdict, Unity Catalog as the governance spine, Databricks SQL warehouses for push-down evaluation, DynamoDB for the state index, Aurora for the catalogue and the debt register, and S3 Object Lock for the override trail.

A developer opens the settings screen, types 0 9 1-5, picks a time zone, and expects a digest to go out every weekday morning. The box is four words wide. Behind it is a durable multi-tenant timer fleet that has to fire tens of millions of independent triggers at the right instant while a partition owner is being replaced, a node's clock is wrong, a zone is draining, and a tenant has just resumed four thousand triggers that slept through a day. This package is that service: the scheduled-trigger platform inside one developer product — an assumed 50,000 tenants, 20 million active triggers, 500 million fires a day at a mean of 5,800 a second, with 60% of all fires landing in the first second of a minute and a design peak of 45,000 a second for five seconds at the top of the hour — built on a globally consistent relational store for the registry, the due index and the fire ledger with commit timestamps as time authority, a rate-shaped task queue for lane-split dispatch, serverless containers for the control, timing and dispatch tiers, a wide-column store for fire history, and a columnar warehouse for lateness and cost reporting.

A coordination service that issues expiring, ordered grants on named resources, built on dedicated etcd Raft quorums on hardware the operator owns, where the fencing token, not the lock, is the real output and mutual exclusion is enforced at the guarded resource's write path.

An Azure-native platform that executes DAG workflows across a horizontally scalable worker fleet, with durable state, at-least-once task delivery, application-level idempotency, configurable retries, dead-letter recovery and per-tenant backpressure. Six decisions carry the design: Cosmos DB is the source of truth and Service Bus only distributes work; a transactional outbox replaces the dual write that would otherwise lose or invent tasks; orchestration is stateless and competing rather than one long-lived actor per execution; retries are re-enqueued as scheduled messages instead of abandoned onto the queue, so backoff and jitter are real; the scheduler is the only leader-elected component and it is fenced with a monotonic token rather than trusted with a lease; and at-least-once is stated as the contract, with idempotency pushed to the task handler and its downstream.

A content delivery platform built from open-source software on hardware the organisation owns: 18 points of presence announcing anycast prefixes, Katran spreading connections across nodes, Envoy terminating TLS and HTTP/3, Apache Traffic Server caching in memory and on NVMe, four shield pools next to the origins, and a control plane in two core data centres that never sits on the request path.

Every workspace product people use daily has grown the same feature: a search box that understands what you meant, and an assistant that answers from your own documents with citations. Notion's workspace search, Slack search, Dropbox Dash, Confluence AI search, Glean across all of them — the visible surface is a box, and the thing that decides whether it works is invisible. This package is that invisible thing, as a shared internal platform inside one collaboration-SaaS company: an assumed 25,000 customer organisations and 3 million monthly active users, 400 million documents, 4.8 billion live chunks, 8 million document versions edited a day, 12,000 retrieval queries a second — built on Kafka as the change log, PostgreSQL as the chunk ledger, MinIO for retained text and snapshots, Redis as the vector cache, a KubeRay GPU fleet addressed by model digest, Qdrant and OpenSearch for retrieval, etcd for aliases, Argo Workflows for rebuilds, and ClickHouse for quality and drift.

A generative search platform is not an LLM in front of a search box. It is a retrieval system with a language model attached to the end of it, and almost every failure it has is a retrieval, authorisation or evidence failure wearing a model's clothes. This package architects one for 45,000 employees over 40 million documents drawn from Microsoft 365, Confluence, ServiceNow, a 12 TB scanned archive, governed Databricks tables and 40 external domains — hybrid retrieval, a bounded agentic loop, claim-level grounding, and continuous evaluation as a release gate.

A multi-tenant, zero-trust identity and access platform on Azure covering human, external and workload identity, authentication, layered authorization, privileged access, governance, secrets and auditability. Five decisions carry the design: a second tenant for customer identity so the boundary is structural rather than procedural; managed identity and workload identity federation so long-lived credentials are eliminated instead of rotated; three separate authorization layers, because an app role can never answer whose data a caller may touch; zero standing privileged access, with PIM as the only path rather than the preferred one; and every role, policy and Conditional Access rule deployed as code with drift reconciled daily.

A metadata platform architected as a connected knowledge layer rather than a database of table and column descriptions. The value is in the relationships — between data assets, business concepts, people, policies, quality and lineage — so the architecture is organised around a canonical model with versioned aspects, one authoritative store with disposable projections, a precedence rule that protects curated metadata from being overwritten by a harvest, and an activation path that pushes classifications back out to the systems that enforce them.

A multi-tenant platform that accepts business events from any internal producer and decides what to notify, to whom, on which channel and when — then composes, dispatches and tracks the result across six channels. Producers publish facts; they know nothing about templates, channels or providers.

When someone opens a delivery app at eight in the evening and sees "arriving in 32 minutes", a model produced that number from a few hundred signals: how busy this restaurant has been in the last fifteen minutes, how long this courier's last three pickups took, what rain does to this neighbourhood on a Friday, how often this user has cancelled. The same signals decide which stores appear first, whether a payment is waved through, and where surge lands. Nine teams own those models. This package is the feature store behind them — for an assumed consumer marketplace of 40M monthly active users, 1.1M partner stores, 180k couriers, 2.2M orders a day peaking at 4,500 a minute, 45 production models and 1,400 features in 120 groups across 6 entity types, built on S3 with Apache Iceberg for the offline store, DynamoDB for the online store, Kinesis with Managed Flink for streaming aggregates, EMR Serverless for batch and backfill, Aurora PostgreSQL for the registry, and gRPC services on EKS behind an internal load balancer.

Everyone has used this system without being told its name. The paperclip in the email client. The drag-and-drop into a chat channel that posts a thumbnail a second later. The folder that syncs from a laptop on hotel wifi and is somehow complete in the morning. The CV uploaded to a company that has never heard of you. The 40 GB video edit dropped into a shared drive the night before a deadline. The screens are easy. What is hard is that each of them is a stranger's bytes entering a system that will later hand them to someone who trusts the system more than they trust the stranger — and the obvious design, scan it inline and store it if it is clean, fails three ways at once: it cannot survive hours of bad network, it cannot survive a scanner that is permanently slower than the uploaders, and it has no answer at all when a signature published tomorrow matches a file declared clean today. This package is the internal upload plane for an assumed collaboration SaaS: 40 million monthly active members across 12 product tenants, 60 million objects a day at a mean of 2.4 MB and a maximum of 50 GB, 150 TB/day of ingress, 12 PB under management — on two Azure Blob storage accounts split into an untrusted and a serving plane, user-delegation SAS as the only upload and download credential, Event Grid and three Service Bus lanes for scan fan-out, ephemeral Container Apps jobs in their own subscription as sandboxed scan workers, Cosmos DB for object state and verdicts, and immutable blob storage for the transition log.

A horizontal multi-vendor marketplace sized to the published Flipkart scale — 500M registered customers, 150M products, 1.4M sellers, 6M orders a day, and sale events assumed at ten times the daily average concentrated into a few hours. Eight decisions carry the design: stock is reserved before money is requested, so a failure lands on inventory rather than on a payment; inventory contention is settled by an atomic Redis compare-and-set with an append-only ledger behind it, because 100,000 buyers on one SKU is a queue with a database attached in any other design; the transactional core is sharded PostgreSQL (Citus) because financial correctness has to be a database guarantee rather than an application convention; cart lives on the server, because the deepest trough on the shopper journey is a basket lost to a UPI redirect; the cardholder data environment is a separate Azure subscription, so PCI scope stops at a boundary that is also an RBAC, network and billing boundary; the event backbone is self-managed Kafka, because every derived store in the estate is rebuilt by replaying it and none of them are backed up; the Buy Box is a precomputed multi-factor score, so delivery reliability competes with price; and what degrades at ten times traffic is a written contract behind feature flags, agreed before the sale rather than invented during it.

When a team chat shows a spinner on send, when a shared document says "reconnecting", when the driver freezes on the map for eight seconds, the user is almost never looking at a crashed service. They are looking at a request routed to a replica that should not have been in rotation — one restarting, one whose connection pool was exhausted, one in a zone already declared unhealthy whose address was still cached in the caller. The opposite failure is less visible and more expensive: a bad health-check config, or a shared database wobbling, and a fleet takes itself out of rotation faster than it can be put back. This package is the internal health and discovery plane that answers two questions continuously for every service-to-service call in an assumed collaboration SaaS: 450 internal services, 60,000 instances, 3 active regions, 12 million internal requests a second at peak, 2,500 instance state changes a minute at steady state and 40,000 during a regional evacuation — on EKS, ECS and EC2 Auto Scaling for registration, Cloud Map and Route 53 for the DNS surface, an Envoy/xDS tier for propagation, Aurora for desired state, DynamoDB for observed state, and ARC for the out-of-band region signal.

An incident management platform for one organisation: 400 responders, 40 services and 25 on-call rotations across three timezones. It receives alerts, decides whether a human must be woken, works out which human, reaches them by push, SMS, voice and email, and keeps escalating until someone acknowledges. Around that loop it keeps an immutable incident timeline, coordinates the response and produces the post-incident review. It is built from open-source software on hardware the organisation owns: HAProxy and Go services at the edge and on the paging path, NATS JetStream for paging state across three sites, Asterisk and Jasmin on two independent carrier routes, PostgreSQL for the append-only event log, ClickHouse for the notification ledger and reporting, and Keycloak and OpenBao for identity and secrets.

The platform that takes a product engineer from "we need a new service" to running, observable, compliant production code without filing a ticket. Today that journey takes nineteen working days and touches seven teams — repository, pipeline, namespace, database, DNS and certificate, secrets, and a security review that arrives after the architecture is already built. This package is the architecture that replaces it: golden paths that scaffold a component in one action, self-service provisioning by declared intent, a shared build and deploy path producing signed and attested artefacts, and guardrails that hold whether or not a team stayed on the road. It is built on AKS as the shared runtime, Entra ID as the identity and entitlement spine, management groups and subscriptions as the tenancy boundary, Terraform against Azure Resource Manager for infrastructure, GitHub Actions for builds, Argo CD for workload reconciliation and a Backstage-derived portal as the developer surface.

A member finishes a lesson, a ride or a round, opens the app, and looks for their own row in a list. That number — the view count under a video, the upvote tally on a post, the weekly league standing, the segment leaderboard, the end-of-year "top 2% of listeners" card — is the most-looked-at piece of software in a consumer product and the one least often designed. This package is the shared platform that produces those numbers for every product team in one company: an assumed 80 million monthly active members across 25 product tenants, 250,000 counting events a second with a 4× burst for 120 seconds, 400,000 leaderboard reads a second split 70/30 between top-N and rank-of-member, 3.5 billion distinct live counters in 120,000 leaderboard scopes, and an event log of 25 TB per 90 days — built on Pub/Sub and a Cloud Storage archive as the record, Dataflow for event-time aggregation, Bigtable for bucket aggregates and versioned ranked views, Spanner for definitions, seasons and sealed standings, Memorystore for the serving cache, and Cloud Run for the request services.

A professional network architected at LinkedIn's published scale: 1.2 billion members, a 270-billion-edge graph served at 2 million queries a second, and trillions of Kafka messages a day. It is built mostly on the systems LinkedIn itself built (Rest.li, Espresso, Venice, LIquid, FollowFeed, Galene, Ambry, Kafka, Brooklin, Samza, Pinot), across 30 views and 33 architecture decision records.

A distributed, multi-tenant admission control plane that sits between applications and LLM providers and answers one question in under 10 milliseconds: may this request proceed? The architecture is organised around four load-bearing decisions — quota is leased to pod-local buckets so most decisions never leave the process, tenant scopes are made atomic by co-locating their keys on one Redis slot, estimated tokens are reserved and reconciled against actuals with a reaper behind them, and the behaviour when coordination fails is a per-tenant policy field rather than a global constant.

The system that decides whether the next version of a consumer AI assistant reaches its 40 million users. Every week the product team produces release candidates — a new base model, a rewritten system prompt, a changed tool definition, a different retrieval configuration — and every one of them is a bet that the assistant got better. This platform turns that bet into evidence: it runs each candidate against versioned datasets, scores the outputs with deterministic checks, a calibrated model judge and human reviewers, compares the result against the incumbent per slice, and emits a release verdict a deployment pipeline is contractually bound by. It is built on GKE for the harness fan-out, Cloud Storage for immutable dataset and trace artefacts, BigQuery as the score store, Pub/Sub as the event spine, and Vertex AI plus an external provider for candidate and judge inference.

A platform that ingests text, PDFs, images, tables, audio and video, and answers natural-language questions across all of it with grounded, cited responses.

Almost everyone who works in an office has built one of these without calling it software. A form submission lands, a row appears in a spreadsheet, a message appears in a chat channel. Nobody wrote code and nobody deployed anything, and it has run unattended for two years — until the Tuesday it posts the same message three times, or the month it quietly stops because somebody in IT revoked a token and no human was told, or the Black Friday morning when 900 submissions trickle through over forty minutes because the spreadsheet API started answering 429. This package is the platform behind that — the Zapier / Make / IFTTT / Power Automate class: an assumed 2,500,000 active workspaces, 8,000 connectors exposing 40,000 actions and triggers, 12,000,000 enabled automations and roughly 1.8 billion step attempts a month; 4,000 trigger events a second accepted at steady state and 12,000 step attempts a second executed, both bursting fourfold; and 2,000,000 polled connections at an average five-minute interval, which is about 6,700 provider polls a second.

The internal platform that answers "is it broken, where, and since when" for 900 services on 12,000 hosts across three AWS regions. It collects metrics, logs and traces, reduces them, stores them on object storage, answers questions about them, and evaluates the alerts that page a human — and then stops, handing a firing alert to the paging platform. It ingests 25 million metric samples, 1.2 million log lines and 2.5 million spans per second at steady state, absorbs 5× that for ten minutes, and is built from OpenTelemetry collection, Amazon MSK as a durable buffer, Grafana Mimir, Grafana Tempo and ClickHouse over S3, DynamoDB for the exemplar index, Aurora PostgreSQL and Git for the control plane, and Amazon Managed Grafana for the consoles.

The release control plane for the words, models and policies that decide what an AI feature says. Every company that has shipped an AI feature has discovered the same thing: the prompt is the product, and the prompt is not in a release. A support assistant answering millions of conversations is governed by a system prompt, a model choice, a tool list, a temperature and a refusal policy — all of which need to change faster than the service reading them ships, and all of which are usually a YAML file that is too slow, a database row edited by hand with no history, or a feature-flag product bent into a shape it was not built for. This one carries 2,000 configuration keys and 25,000 published versions across roughly 300 consuming deployments, resolving 40,000 times a second, on Forgejo and an OCI registry for artefacts, etcd for pointers, NATS JetStream for propagation, PostgreSQL for authoring, ClickHouse for exposures, OpenBao for signing and SPIRE for workload identity.

You search a delivery app at 8pm and the first restaurant is closed. You search a marketplace for earbuds, tap the third result, and it is out of stock. You rename a file in a workspace tool and the old name keeps coming back for a minute. None of those are ranking failures — the cluster answered correctly from a copy of the world that was wrong. This package is the platform that keeps that copy right: a multi-tenant search indexing service inside one consumer marketplace — an assumed 40 million monthly active users, 180,000 active merchants, 12 product tenants, 80 million searchable documents across 40 indices, 25,000 change events a second rising to 100,000 in a burst, 15,000 queries a second rising to 45,000 at the dinner hour, and a largest index of 38 million documents that has to rebuild inside four hours — built on a managed durable change log, a managed search cluster, strongly consistent assembly state, and a relational registry holding every index definition the platform has ever served.

The service a workload calls before its first query, and again before its credential lapses. It proves what the workload is, decides what it may have, mints a credential or releases a custodial secret, delivers it to the process, and makes sure it expires without anyone's help, with revocation by scope as the fast path. It serves 40,000 workload instances across three environments and two data centres the organisation owns. It is built from open-source software: SPIRE for attested workload identity; OpenBao split into separate KMS, custody and broker clusters per environment and site, with the KMS on an HSM seal run by key custodians; a stateless issuance gateway and a per-node credential agent built in Go; Kafka, ClickHouse and Ceph Object Lock for the audit record; and a Forgejo registry from which every policy is compiled.

A service mesh for 2,000 services and 40,000 proxies across 20 Kubernetes clusters in three data centres the organisation owns. It is built from open-source software: Istio with Envoy sidecars for the data plane and configuration compiler, SPIRE for attested workload identity under an offline HSM root with per-cluster intermediates in OpenBao, Cilium for pod networking and a second enforcement plane, Argo CD and Argo Rollouts for intent and weights, and Prometheus, Thanos, Tempo and ClickHouse for telemetry.

It is Monday morning. The status page is green, the dashboards are green, and a dozen weekend tickets say checkout failed on Saturday night. The release train is loaded and someone has to decide whether it ships. In most organisations that decision is made by whoever is most senior in the room. This package is the platform that replaces the argument with arithmetic: for each critical user journey — sign in, search, add to cart, checkout, send notification, start video playback — it holds a declared reliability target, measures what users actually got, and publishes the difference as an error budget with a typed verdict attached. The assumed operating context is 1,200 SLOs across 400 services and 60 journeys, growing 30% a year, with 1.73 million minute buckets written a day and a verdict API serving 400 requests a second steady state and 2,000 for two minutes during a coordinated release window — built on Azure Data Explorer for minute-grain counters, Azure SQL as the registry and tamper-evident audit, Event Hubs for the indicator stream, Container Apps for every tier, and a Managed HSM for the one key that signs a verdict.

A platform service that runs interruptible work on capacity the organisation's own private cloud can take back, and keeps that work running well enough to be trusted. It chooses which pools of reclaimable capacity each workload runs on, drains and checkpoints work inside a 120-second notice, replaces what was lost, falls back to guaranteed capacity when a workload's declared contract would otherwise be breached, and reports the saving net of everything it cost. It serves 220 workloads on 12,000 to 40,000 vCPU across three regions the organisation owns. It is built from open-source software: OpenStack (Nova, Placement, Ironic, Keystone, Blazar) as the capacity provider; a node agent and partitioned controllers built in Go; Kubernetes with Kyverno and Slurm as the schedulers that keep placing the work; Ceph RGW for checkpoints, with keys in OpenBao; PostgreSQL with Patroni for fleet state; Kafka, Vector and ClickHouse for lifecycle events and the savings ledger; and Forgejo with CUE for contracts.

A placement service for the 9.2 billion objects a file-collaboration product holds, built from open-source software in two data centres the organisation owns: Vitess on MySQL for the placement catalogue, Ceph RGW for the hot, warm and cold tiers, EOS and the CERN Tape Archive for tape, Kafka and ClickHouse for access telemetry, Temporal for policy rollout and recall jobs, and SPIRE, OpenBao and Keycloak for identity and keys.

The service behind every short link anybody has ever tapped without thinking about it: bit.ly/…, lnkd.in/…, the tracked link in a marketing email, the QR code on a conference badge. A customer submits a destination and receives a short code on a domain they control; every click on that code is resolved and redirected in single-digit milliseconds, anywhere in the world, and every click is counted. It handles 150,000 redirects per second at steady state and 600,000 at peak across twelve read regions, built from Azure Front Door for anycast termination and a 30-second response cache, Cosmos DB for the globally replicated link table with native TTL, Azure Cache for Redis for the per-region hot set, Container Apps for the resolver and management planes, Event Hubs for the click stream and Azure Data Explorer for click analytics.

The mechanism behind every paymentintent.succeeded, pullrequest.opened and order/create a SaaS product ever sent: the service that turns a committed domain event into a signed HTTPS request against 40,000 endpoints nobody here controls, keeps trying for 72 hours when one of them is having a bad day, and lets the developer who owns it find out why theirs is not receiving anything — without opening a support ticket. It handles 25,000 accepted events per second and 45,000 delivery attempts per second across two regions, built from SQS FIFO for per-endpoint isolation and redrive, Fargate delivery workers in egress-only subnets behind a published NAT range, DynamoDB for subscriptions and the attempt log, S3 for payloads, and KMS for per-endpoint signing material.